Virtual footwear try-on (VFTON), a critical yet underexplored area in virtual try-on (VTON), aims to synthesize faithful try-on results given diverse footwear and model images while maintaining 3D consistency and texture authenticity. Unlike conventional garment-focused VTON methods, VFTON presents unique challenges due to (1) Data Scarcity, which arises from the difficulty of perfectly matching product shoes with models wearing the identical ones, (2) Viewpoint Misalignment, where the target foot pose and source shoe views are always misaligned, leading to incomplete texture information and detail distortion, and (3) Background-induced Color Distortion, where complex material of footwear interacts with environmental lighting, causing unintended color contamination. To address these challenges, we introduce MVShoes, a multi-view shoe try-on dataset consisting of 7305 well-annotated image triplets, covering diverse footwear categories and challenging try-on scenarios. Furthermore, we propose a dual-stream DiT architecture, ShoeFit, designed to mitigate viewpoint misalignment through Multi-View Conditioning with 3D Rotary Position Embedding, and alleviate background-induced distortion using the LayeredRefAttention which leverages background features to modulate footwear latents. The proposed framework effectively decouples shoe appearance from environmental interferences while preserving high-quality texture detail through decoupled denoising and conditioning branches. Extensive quantitative and qualitative experiments demonstrate that our method substantially improves rendering fidelity and robustness under challenging real-world product shoes, establishing a new benchmark in high-fidelity footwear try-on synthesis. The dataset and benchmark will be publicly available upon acceptance of the paper.

Currently, multimedia systems and computer vision algorithms are increasingly playing a crucial role in biological research. However, due to the significant difference between macro and micro scenarios, it is impractical to directly transfer existing computer vision methods to the images captured by microscopes. Taking social behavior analysis of worm for example, it heavily depends on accurate and efficient Multi-object tracking (MOT) methods. Meanwhile, it faces great challenges due to the unique physical characteristics of worm, such as small size, highly uniform appearance, rapid deformation and overlapping movement. This paper studies on the challenges and existing solutions for MOT in worm crowds by building a well-designed dataset ("WormTrack") and a tracking-by-detection benchmark. We observed that the state-of-the-art MOT methods suffers from considerable performance drop on the new dataset. Therefore, we propose a customized MOT method for worm crowds by deeply understanding the physical characteristics of worms and scenes. The method is composed by an instance segmentation based detector, a multiple model fused Kalman filter based tracker and a multi-constraint based trajectory repairer. The experimental results demonstrate that our method can accurately track over 100 worms with almost identical appearance for a long period, which is exceptional compared to existing methods. We hope our work will attract further researches to explore more in this new field, and promote the crossing field researches with biology and medicine. Our code and data is available at https://github.com/Jeerrzy/wormstudio.

Diffusion models (DMs) have recently gained attention with state-of-the-art performance in text-to-image synthesis. Abiding by the tradition in deep learning, DMs are trained and evaluated on the images with fixed sizes. However, users are demanding for various images with specific sizes and various aspect ratio. This paper focuses on adapting text-to-image diffusion models to handle such variety while maintaining visual fidelity. First we observe that, during the synthesis, lower resolution images suffer from incomplete object portrayal, while higher resolution images exhibit repetitively disordered presentation. Next, we establish a statistical relationship indicating that attention entropy changes with token quantity, suggesting that models aggregate spatial information in proportion to image resolution. The subsequent interpretation on our observations is that objects are incompletely depicted due to limited spatial information for low resolutions, while repetitively disorganized presentation arises from redundant spatial information for high resolutions. From this perspective, we propose a scaling factor to alleviate the change of attention entropy and mitigate the defective pattern observed. Extensive experimental results validate the efficacy of the proposed scaling factor, enabling models to achieve better visual effects, image quality, and text alignment. Notably, these improvements are achieved without additional training or fine-tuning techniques.